lack of association between polymorphisms in the cyp1a2

RESEARCH ARTICLE Open Access

Lack of association betweenpolymorphisms in the CYP1A2 gene andrisk of cancer: evidence from meta-analysesVladimir Vukovic*, Carolina Ianuale, Emanuele Leoncini, Roberta Pastorino, Maria Rosaria Gualano,Rosarita Amore and Stefania Boccia

Abstract

Background: Polymorphisms in the CYP1A2 genes have the potential to affect the individual capacity to convertpre-carcinogens into carcinogens. With these comprehensive meta-analyses, we aimed to provide a quantitativeassessment of the association between the published genetic association studies on CYP1A2 single nucleotidepolymorphisms (SNPs) and the risk of cancer.

Methods: We searched MEDLINE, ISI Web of Science and SCOPUS bibliographic online databases and databases ofgenome-wide association studies (GWAS). After data extraction, we calculated Odds Ratios (ORs) and 95 %confidence intervals (CIs) for the association between the retrieved CYP1A2 SNPs and cancer. Random effect modelwas used to calculate the pooled ORs. Begg and Egger tests, one-way sensitivity analysis were performed, whenappropriate. We conducted stratified analyses by study design, sample size, ethnicity and tumour site.

Results: Seventy case-control studies and one GWA study detailing on six different SNPs were included. Amongthe 71 included studies, 42 were population-based case-control studies, 28 hospital-based case-control studies andone genome-wide association study, including total of 47,413 cancer cases and 58,546 controls. The meta-analysisof 62 studies on rs762551, reported an OR of 1.03 (95 % CI, 0.96–1.12) for overall cancer (P for heterogeneity < 0.01;I2 = 50.4 %). When stratifying for tumour site, an OR of 0.84 (95 % CI, 0.70–1.01; P for heterogeneity = 0.23, I2 = 28.5 %)was reported for bladder cancer for those homozygous mutant of rs762551. An OR of 0.79 (95 % CI, 0.65–0.95; P forheterogeneity = 0.09, I2 = 58.1 %) was obtained for the bladder cancer from the hospital-based studies and onCaucasians.

Conclusions: This large meta-analysis suggests no significant effect of the investigated CYP1A2 SNPs on cancer overallrisk under various genetic models. However, when stratifying according to the tumour site, our results showed aborderline not significant OR of 0.84 (95 % CI, 0.70–1.01) for bladder cancer for those homozygous mutant of rs762551.Due to the limitations of our meta-analyses, the results should be interpreted with attention and need to be furtherconfirmed by high-quality studies, for all the potential CYP1A2 SNPs.

Keywords: CYP1A2, Polymorphism, Cancer, Meta-analysis, Susceptibility

* Correspondence: [email protected] of Public Health- Section of Hygiene, Università Cattolica del SacroCuore, Largo F.Vito 1, 00168 Rome, Italy

© 2016 Vukovic et al. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link tothe Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Vukovic et al. BMC Cancer (2016) 16:83 DOI 10.1186/s12885-016-2096-5

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BackgroundCancer is a complex disease that develops as a result ofthe interactions between environmental factors and gen-etic inheritance. In 2012 there were 14.1 million newcancer cases and 8.2 million cancer deaths worldwide[1]. Endogenous or exogenous xenobiotics are activatedor inactivated through two metabolic steps by phase Iand phase II enzymes [2]. The majority of chemical car-cinogens require activation to electrophilic reactiveforms to produce DNA adducts and this is mainly cata-lyzed by phase I enzymes. Although there are some ex-ceptions, phase II enzymes, in contrast, detoxify suchintermediates through conjugative reactions. The conse-quent formation of reactive metabolites and their bind-ing to DNA to give stable adducts are considered to becritical in the carcinogenic process. It might therefore beexpected that individuals with increased activation orlow detoxifying potential have a higher susceptibility forcancer [3].Cytochrome P450 1A2 (CYP1A2) enzyme is a member

of the cytochrome P450 oxidase system and is involvedin the phase I metabolism of xenobiotics. In humans,the CYP1A2 enzyme is encoded by the CYP1A2 gene[4]. In vivo, CYP1A2 activity exhibits a remarkable de-gree of interindividual variations, as the gene expressionis highly inducible by a number of dietary and environ-mental chemicals, including tobacco smoking, hetero-cyclic amines (HAs), coffee and cruciferous vegetables.Another possible contributor to interindividual variabil-ity in CYP1A2 activity is the occurrence of polymor-phisms in the CYP1A2 gene [5], which have thepotential for determining individual’s different suscepti-bility to carcinogenesis [6]. CYP1A2 is expressed mainlyin the liver, but also, expression of the CYP1A2 enzymein pancreas and lung has been detected. The CYP1A2gene consists of 7 exons and is located at chromosome15q22-qter. More than 40 single nucleotide polymor-phisms (SNPs) of the CYP1A2 gene have been discov-ered so far [7, 8].High in vivo CYP1A2 activity has been suggested to be

a susceptibility factor for cancers of the bladder, colon andrectum, where exposure to compounds such as aromaticamines and HAs has been implicated in the etiology ofthe disease [5, 6]. Additionally, it has been reported thatamong the CYP1A2 polymorphisms, CYP1A2*1C(rs2069514) and CYP1A2*1 F (rs762551) are associatedwith reduced enzyme activity in smokers [5].In recent years, efforts have been put into investi-

gating the association of CYP1A2 polymorphisms andthe risk of several cancers, among them, colorectal[9–23], lung [7, 24–32], breast [33–46], bladder [4,47–52], and other in different population groups, withinconsistent results. Therefore, with these meta-analyses we aimed to provide a quantitative

assessment of the association between all CYP1A2polymorphisms and risk of cancer at various sites.

MethodsSelection criteriaIdentification of the studies was carried out through asearch of MEDLINE, ISI Web of Science and SCOPUSdatabases up to February 15th, 2015, by two independentresearchers (R.A. and V.V.). The following terms wereused: [(Cytochrome P450 1A2) OR (CYP1A2)] AND(Cancer) AND (Humans [MeSH]), without any restric-tion on language. All eligible studies were retrieved, andtheir bibliographies were hand-searched to find add-itional eligible studies. We only included published stud-ies with full-text articles available.Also, detail search of several publically available data-

bases of genome-wide association studies (GWAS) -GWAS Central, Genetic Associations and Mechanismsin Oncology (GAME-ON), the Human Genome Epi-demiology (HuGE) Navigator, National Human GenomeResearch Institute (NHGRI GWAS Catalog), The data-base of Genotypes and Phenotypes (dbGaP), TheGWASdb, VarySysDB Disease Edition (VaDE), The gen-ome wide association database (GWAS DB), was carriedout up to February 15th, 2015 for the association be-tween CYP1A2 and various cancers using the combina-tions of following terms: (Cytochrome P450 1A2) OR(CYP1A2) OR (Chromosome 15q24.1) AND (Cancer).Additional consultation of principal investigators (PI) ofthe retrieved GWAS was undertaken in order to obtainthe primary data and include them in the analyses.Studies were considered eligible if they were assessing

the frequency of any CYP1A2 gene polymorphism in re-lation to the number of cancer cases and controls, ac-cording to the three variant genotypes (wild-typehomozygous (wtwt), heterozygous (wtmt) and homozy-gous mutant (mtmt)). Case-only and case series studieswith no control population were excluded, as well asstudies based only on phenotypic tests, reviews, meta-analysis and studies focused entirely on individualsyounger than 16 years old. When the same sample wasused in several publications, we only considered themost recent or complete study to be used in our meta-analyses. Meanwhile, for studies that investigated moretypes of cancer, we counted them as individual data onlyin a subgroup analysis by the tumour type, while whenthey reported different ethnicity or location within thesame study, we considered them as a separate studies.

Data extractionTwo investigators (C.I. and V.V.) independently ex-tracted the data from each article using a structuredsheet and entered them into the database. The followingitems were considered: rs number, first author, year and

Vukovic et al. BMC Cancer (2016) 16:83 of 17

location of the study, tumour site, ethnicity, study de-sign, number of cases and controls, number of heterozy-gous and homozygous individuals for the CYP1A2polymorphisms in the compared groups. We used widelyaccepted National Center for Biotechnology Information(NCBI) CYP classification [53] to determine which spe-cific genotype should be considered as wtwt, wtmt andmtmt. We also ranked studies according to their samplesize, where studies with minimum of 200 cases wereclassified as small and above 200 cases as large.

Statistical analysisThe estimated Odds Ratios (ORs) and 95 % confidenceinterval (CI) for the association between each CYP1A2SNP and cancer were defined as follows:

� wtmt vs wtwt (OR1)� mtmt vs wtwt (OR2).

According to the following algorithm on the criteria toidentify the best genetic model [54] for each SNP:

� Recessive model (mtmt versus wt carriers): if OR2 ≠1 and OR1 = 1

� Dominant model (mt carriers versus wtwt): if OR2 =OR1 ≠ 1,

we used the dominant model of inheritance forrs2069514, rs2069526 and rs35694136 and recessivemodel for rs762551, rs2470890 and rs2472304 in themeta-analysis. Random effect model was used to calcu-late the pooled ORs, taking into account the possibilityof between studies heterogeneity [55], that was evaluatedby the χ2-based Q statistics and the I2 statistics [56],where I2 = 0 % indicates no observed heterogeneity,within 25 % regarded as low, 50 % as moderate, and75 % as high [57]. A visual inspection of Begg’s funnelplot and Begg’s and Egger’s asymmetry tests [58] wereused to investigate publication bias, where appropriate[59]. To determinate the deviation from the Hardy-Weinberg Equilibrium (HWE) we used a publicly avail-able program (http://ihg.gsf.de/cgi-bin/hw/hwa1.pl ).Additionally, the Galbraith’s test [60] was performed toevaluate the weight each study had on the overall esti-mate and its contribution on Q-statistics. We also per-formed a one-way sensitivity analysis to explore theeffect that each study had on the overall effect estimate,by computing the meta-analysis estimates repeatedlyafter every study has been omitted.Studies whose allele frequency in the control popula-

tion deviated significantly from the Hardy-WeinbergEquilibrium (HWE) at the p-value ≤ 0.01 were excludedfrom the meta-analyses, given that this deviation mayrepresent bias. We conducted stratified analysis by study

design, ethnicity, sample size and tumour site to investi-gate the potential sources of heterogeneity across thestudies. Statistical analyses were performed using theSTATA software package v. 13 (Stata Corporation, Col-lege 162 Station, TX, USA), and all statistical tests weretwo-sided.

ResultsCharacteristics of the studiesWe identified a total of 2541 studies through MEDLINE,ISI Web of Science and SCOPUS online databases. Onethousand and sixteen studies were left after duplicatesremoval, and after carefully reading the titles, only 175studies were assessed for eligibility. After reviewing theabstracts, 120 full text articles were obtained for furthereligibility. By not fulfilling the inclusion criteria, 61 fulltext articles were excluded, leaving 59 studies for quanti-tative synthesis. Additional hand-search of the referencelists of 59 included studies was done and 11 new eligiblestudies were found, resulting in 70 included studies.Eleven GWASs on the association between CYP1A2

SNPs and cancer risk were identified after detail searchof GWAS online databases. Studies did not report fulldata on investigated SNPs, so we contacted principal in-vestigators (PIs) to retrieve the information and includeinto our analyses. After 3 repeated solicitations, only onePI provided us with the full data on CYP1A2 SNPs ofbreast cancer cases and controls, and by this makingtotal of 71 studies included in our meta-analyses [4, 7–52, 61–84]. Figure 1 shows the process of literaturesearch and study selection.Among the 71 included studies, 42 were population-

based case-control studies, 28 hospital-based case-control studies and one genome-wide association study,including total of 47,413 cancer cases and 58,546 con-trols (Table 1). The total investigated SNPs were six, ofwhich 62 studies on the rs762551 [4, 7–21, 23, 24, 26–46, 48–50, 52, 61–65, 67, 68, 72–75, 77–79, 81–84].Thirty five studies out of 62 were conducted on Cauca-sians (56.5 %), 17 on mixed populations (27.4 %) and 10on Asians (16.1 %), including 33,181 cancer cases and40,195 controls. Among them, 15 were on breast cancer,14 studies on colorectal, and 9 on lung cancer.Twenty studies investigated the rs2069514 [9, 16, 18,

22–27, 29–32, 34, 47, 51, 61, 66, 71, 76], of which 11were conducted on Caucasians (55 %) and 9 on Asians(45 %). Eight studies investigated the effect on lung can-cer (40 %), 5 studies on colorectal cancer (25 %), 2 onliver cancer (10 %), 2 on bladder (10 %) and by 1 studyon stomach (5 %), breast (5 %) and pleura (5 %), totalingfor 4562 cancer cases and 6399 controls (Table 1).The remaining four SNPs were investigated by a re-

duced number of studies and details are presented inTable 1. Genotype frequencies in all control groups did


http://ihg.gsf.de/cgi-bin/hw/hwa1.pl

not deviate from values predicted by HWE (Table 1). Assome studies on different cancer types shared the samecontrol group [35], these studies were aggregated whenperforming the meta-analyses, except when stratified bytumour site.

Quantitative synthesisAs the crude analysis for rs762551 provided an OR1 of1.03 (95 % CI 0.98–1.07) and an OR2 of 1.06 (95 % CI0.97–1.16), for rs2470890 OR1 1.03 (95 % CI 0.93–1.14)and OR2 of 1.14 (95 % CI 0.97–1.34) and for rs2472304OR1 of 0.98 (95 % CI 0.79–1.22) and OR2 of 0.89 (95 %CI 0.66–1.22) according to the criteria proposed in themethods section, we applied the recessive model of in-heritance for the meta-analyses. On the other hand, forrs2069514, rs2069526 and rs35694136 original papers

did not report enough data to calculate OR1 and OR2,so we were able only to apply the dominant model forthe data analyses.The Figs. 2 and 3 depict the forest plots of the ORs of

the six CYP1A2 SNPs and cancer. By pooling 62 studieson rs762551, the meta-analysis reported an OR of 1.03(95 % CI 0.96–1.12) for overall cancer (P for heterogen-eity < 0.01; I2 = 50.4 %). Egger test and the Begg’s correl-ation method did not provide statistical evidence ofpublication bias (P = 0.19 and P = 0.39, respectively)(Fig. 4). To explore the potential sources of heterogen-eity, we performed the Galbraith’s test which identifiedthe study of Shimada N. (b) [45] and Sangrajrang S. [44],as the main contributors to heterogeneity (graph notshown). In the one-way sensitivity analysis, these twooutlying studies were omitted from meta-analysis and

Fig. 1 Flowchart depicting literature search and study selection. *GWAS data bases searched: GWAS Central, Genetic Associations andMechanisms in Oncology (GAME-ON), the Human Genome Epidemiology (HuGE) Navigator, National Human Genome Research Institute (NHGRIGWAS Catalog), The database of Genotypes and Phenotypes (dbGaP), The GWASdb, VarySysDB Disease Edition (VaDE), The genome wideassociation database (GWAS DB)


Table 1 Description of 45 studies included in meta-analysis of association between different CYP1A2 SNPs and cancer

Rs number First author Year Tumour site Country Ethnicity Sample size(No. cases/controls)

Crude OR° (95 % CI)recessive model

Crude OR(95 % CI)dominant model

rs762551 Goodman MT. [73] 2001 Ovaries USA Mixed 116/138*a 0.52 (0.19–1.43) –

Sachse C. [18] 2002 Colorectum UK Caucasian 490/593*ª 1.15 (0.70–1.88) –

Goodman MT. [74] 2003 Ovaries USA Mixed 164/194*ª 0.73 (0.34–1.55) –

Hopper J. [36] 2003 Breast Australia Caucasian 204/287*c 0.55 (0.27–1.13) –

Doherty JA. [68] 2005 Endometrium USA Mixed 371/420*ª 1.27 (0.75–2.15) –

Landi S. [16] 2005 Colorectum Spain Caucasian 361/321*b 1.74 (1.05–2.88) –

Le Marchand L.[39]

2005 Breast USA Mixed 1339/1369*a 0.73 (0.55–0.96) –

Prawan A. [81] 2005 Liver Thailand Asian 216/233*a 0.52 (0.24–1.13) –

Mochizuki J. [79] 2005 Liver Japan Asian 31/123*a 1.35 (0.26–7.01) –

Agudo A. [61] 2006 Stomach Europeancountries1

Caucasian 242/943*a 0.88 (0.50–1.55) –

Bae SY. [9] 2006 Colorectum S. Korea Asian 111/93*b 1.14 (0.51–2.54) –

De Roos AJ. [67] 2006 Lymphoma USA Mixed 745/640*a 0.91 (0.63–1.31) –

Li D. [8] 2006 Pancreas USA Mixed 307/333*b 1.10 (0.65–1.84) –

Long JR. [41] 2006 Breast China Asian 1082/1139*a 0.89 (0.71–1.13) –

Rebbeck TR [82] 2006 Endometrium USA Mixed 475/1233*a 1.03 (0.73–1.46) –

Kiss I. [13] 2007 Colorectum Hungary Caucasian 500/500*b 1.07 (0.74–1.54) –

Kury S. [15] 2007 Colorectum France Caucasian 1013/1118*a 1.03 (0.75–1.41) –

Osawa Y. [29] 2007 Lung Japan Asian 103/111*a 1.17 (0.57–2.42) –

Takata Y. [46] 2007 Breast USA (Hawaii) Mixed 325/250*a 0.76 (0.39–1.49) –

Yoshida K. [23] 2007 Colorectum Japan Asian 64/111*a 0.57 (0.21–1.53) –

Gemignani F. [26] 2007 Lung Europeancountries2

Caucasian 297/310*b 0.86 (0.50–1.49) –

Kotsopoulos J. [38] 2007 Breast Canada Caucasian 170/241*b 2.12 (0.99–4.57) –

Gulyaeva LF. [35] 2008 Endometrium Russia Caucasian 166/180*a 2.20 (0.40–12.16) –

Gulyaeva LF. [35] 2008 Ovaries Russia Caucasian 96/180*a 9.21 (1.95–43.53) –

Gulyaeva LF. [35] 2008 Breast Russia Caucasian 93/180*a 27.58(6.32–120.35)

–

Hirata H. [75] 2008 Endometrium USA Caucasian 150/165*a 0.96 (0.62–1.51) –

Saebo M. [19] 2008 Colorectum Norway Caucasian 198/222*a 1.05 (0.49–2.23) –

Suzuki H. [84] 2008 Pancreas USA Caucasian 649/585*a 0.93 (0.56–1.54) –

Figueroa JD [48] 2008 Bladder Spain Caucasian 1101/1021*b 0.80 (0.62–1.04) –

Zienolddiny S. [32] 2008 Lung Norway Caucasian 335/393*a 1.43 (0.88–2.32) –

Cotterchio M. [11] 2008 Colorectum Canada Caucasian 835/1247*a 0.91 (0.67–1.23) –

Aldrich MC. [7] 2009 Lung USA Mixed 113/299*a 3.36 (1.58–7.13) –

Altayli E. [4] 2009 Bladder Turkey Caucasian 135/128*b 1.51 (0.88–2.60) –

B’chir F. [24] 2009 Lung Tunisia Caucasian 101/98*b 0.90 (0.47–1.70) –

Kobayashi M. [78] 2009 Stomach Japan Asian 141/286*b 0.62 (0.33–1.18) –

Kobayashi M. [14] 2009 Colorectum Japan Asian 104/225*b 0.64 (0.31–1.32) –

Shimada N (a) [45] 2009 Breast Japan andBrazil

Asian 483/484*b 1.02 (0.71–1.47) –

Shimada N (b) [45] 2009 Breast Brazil Mixed 389/389*b 0.50 (0.31–0.80) –

Sangrajrang S. [44] 2009 Breast Thailand Asian 552/483*b 2.72 (1.52–4.86) –

Villanueva C. [52] 2009 Bladder Spain Caucasian 1034/911*b 0.82 (0.62–1.07) –


Table 1 Description of 45 studies included in meta-analysis of association between different CYP1A2 SNPs and cancer (Continued)

Canova C. [64] 2009 UADT Europeancountries3

Caucasian 1480/1437*b 0.88 (0.69–1.13) –

Cleary SP [10] 2010 Colorectum Canada Caucasian 1165/1290*a 0.93 (0.71–1.22) –

Pavanello S. [50] 2010 Bladder Italy Caucasian 155/161*b 0.57 (0.25–1.30) –

Singh A. [31] 2010 Lung India Caucasian 200/200*a 0.61 (0.37–1.00) –

The MARIE-GENICAConsortium [43]

2010 Breast Germany Caucasian 3147/5485*a 1.04 (0.88–1.22) –

Canova C. [65] 2010 UADT Italy Caucasian 376/386*b 1.21 (0.77–1.89) –

Ashton KA [62] 2010 Endometrium Australia Caucasian 191/291*a 1.03 (0.71–1.49) –

Guey LT [49] 2010 Bladder Spain Caucasian 1005/1021*b 0.77 (0.58–1.00) –

Rudolph A. [17] 2011 Colorectum Germany Caucasian 678/680*a 1.38 (0.93–2.05) –

Sainz J. [20] 2011 Colorectum Germany Caucasian 1764/1786*a 0.95 (0.75–1.19) –

Jang JH [77] 2012 Pancreas Canada Mixed 447/880*a 1.08 (0.73–1.59) –

Khvostova EP [37] 2012 Breast Russia Caucasian 323/526*b 1.82 (1.14–2.90) –

Pavanello S. [30] 2012 Lung Denmark Caucasian 421/776*a 1.63 (1.08–2.48) –

Wang J. [21] 2012 Colorectum USA Mixed 305/357*a 0.97 (0.55–1.70) –

Anderson LN [33] 2012 Breast Canada Mixed 886/932*a 1.50 (1.09–2.07) –

Ayari I. [34] 2013 Breast Tunisia Caucasian 117/42*b 1.62 (0.51–5.11) –

Barbieri RB [63] 2013 Thyroid gland Brasil Mixed 123/339*a 2.12 (1.16–3.87) –

Dik VK [12] 2013 Colorectum TheNetherlands

Caucasian 970/1590*a 1.10 (0.85–1.43) –

Gervasini G. [27] 2013 Lung Spain Caucasian 95/196*b 1.25 (0.60–2.61) –

Lee HJ. [40] 2013 Breast USA Mixed 579/981*a 1.22 (0.85–1.75) –

Lowcock E. [42] 2013 Breast Canada Mixed 1693/1761*a 1.24 (0.97–1.57) –

Ghoshal U. [72] 2014 Stomach India Caucasian 88/170*a 1.13 (0.57–2.22) –

Mikhalenko AP.[28]

2014 Lung Belarus Caucasian 92/328*a 1.14 (0.44–2.93) –

Shahabi A. [83] 2014 Prostate USA Mixed 1480/777*a 0.97 (0.72–1.30) –

rs2069514 Sachse C. [18] 2002 Colorectum UK Caucasian 60/73*a – 12.71(1.56–103.44)

Tsukino H. [51] 2004 Bladder Japan Asian 306/306*a – 0.95 (0.69–1.31)

Landi S. [16] 2005 Colorectum Spain Caucasian 328/295*b – 0.90 (0.38–2.10)

Chiou HL [25] 2005 Lung China Asian 162/208*b – 1.04 (0.69–1.57)

Agudo A. [61] 2006 Stomach Europeancountries1

Caucasian 243/945*a – 1.66 (0.72–3.84)

Chen X. [66] 2006 Liver China Asian 430/546*a – 0.97 (0.75–1.24)

Bae SY. [9] 2006 Colorectum S. Korea Asian 111/93*b – 0.68 (0.39–1.18)

Yoshida K. [23] 2007 Colorectum Japan Asian 66/113*a – 0.82 (0.44–1.52)

Osawa Y. [29] 2007 Lung Japan Asian 106/113*a – 0.80 (0.46–1.36)


Caucasian 278/294*b – 0.52 (0.16–1.75)

Zienolddiny S. [32] 2008 Lung Norway Caucasian 243/214*a – 0.65 (0.22–1.91)

Imaizumi T. [76] 2009 Liver Japan Asian 209/256*a – 0.88 (0.61–1.27)

B’chir F. [24] 2009 Lung Tunisia Caucasian 101/98*b – 5.88(2.96–11.70)

Yeh CC [22] 2009 Colorectum Taiwan Asian 718/731*b – 1.08 (0.88–1.32)

Gemignani F. [71] 2009 Pleura Italy Caucasian 92/643*b – 0.33 (0.04–2.45)

Singh A. [31] 2010 Lung India Caucasian 200/200*a – 0.84 (0.47–1.50)


the overall OR slightly changed to 1.03 (95 % CI 0.96–1.11), with a reduced heterogeneity (P for heterogeneity<0.01; I2 = 43.0 %).Results of the stratified meta-analyses are reported in

the Table 2. When stratifying the results of meta-analysisfor rs762551 by ethnicity, we found no significant effectof CYP1A2 on cancer risk for Caucasians (OR = 1.03;

95 % CI 0.94–1.13), Asians (OR = 0.95; 95 % CI 0.72–1.27) nor among a mixed population (OR = 1.05; 95 %CI 0.89–1.25). When stratifying according to the tumoursite, results showed an OR of 0.84 (95 % CI 0.70–1.01; Pfor heterogeneity = 0.23, I2 = 28.5 %) for bladder cancerfor those homozygous mutant types of rs762551(Table 2). We further examined the association between

Table 1 Description of 45 studies included in meta-analysis of association between different CYP1A2 SNPs and cancer (Continued)

Pavanello S. [30] 2012 Lung Denmark Caucasian 423/777*a – 0.85 (0.32–2.24)

Ayari I. [34] 2013 Breast Tunisia Caucasian 109/41*b – 0.35(0.14–0.90)

Gervasini G. [27] 2013 Lung Spain Caucasian 95/196*b – 2.67 (0.70–10.17)

Cui X. [47] 2013 Bladder Japan Asian 282/257*b – 0.89 (0.63–1.26)

rs2069526 Sachse C. [18] 2002 Colorectum UK Caucasian 490/593*a – 0.86 (0.60–1.22)

Landi S. [16] 2005 Colorectum Spain Caucasian 321/288*b – 1.27 (0.55–2.90)


Caucasian 247/251*b – 0.34(0.14–0.81)

Zienolddiny S. [32] 2008 Lung Norway Caucasian 194/239*a – 1.66 (0.37–7.49)

Gemignani F. [71] 2009 Pleura Italy Caucasian 78/579*b – 1.10 (0.42–2.90)

Singh A. [31] 2010 Lung India Caucasian 200/200*a – 1.07 (0.65–1.75)

Gervasini G. [27] 2013 Lung Spain Caucasian 95/196*b – 1.36 (0.57–3.27)

rs2470890 Hopper J. [36] 2003 Breast Australia Caucasian 204/287*c 0.82 (0.47–1.43) –

Landi S. [16] 2005 Colorectum Spain Caucasian 353/320*b 1.24 (0.84–1.82) –

Chen X. [66] 2006 Liver China Asian 428/545*a 0.53 (0.27–1.06) –

Kury S. [15] 2007 Colorectum France Caucasian 1013/1118*a 1.07 (0.90–1.27) –


Caucasian 283/298*b 0.83 (0.51–1.35) –


Gemignani F. [71] 2009 Pleura Italy Caucasian 85/669*b 1.02 (0.56–1.88) –

Canova C. [64] 2009 UADT Europeancountries3

Caucasian 1455/1403*b 1.03 (0.84–1.26) –

Canova C. [65] 2010 UADT Italy Caucasian 374/387*b 1.51 (1.02–2.23) –

Anderson LN [33] 2012 Breast Canada Mixed 884/927*a 1.49 (1.18–1.89) –

Eom SY. [69] 2013 Stomach S. Korea Asian 473/472*b 1.15 (0.55–2.37) –

rs2472304 Hopper J. [36] 2003 Breast Australia Caucasian 204/286*c 0.81 (0.46–1.43) –

Sangrajrang S. [44] 2009 Breast Thailand Asian 552/478*b 1.16 (0.59–2.29) –


Ferlin A. [70] 2010 Testicles Italy Caucasian 234/218*a 0.68 (0.46–1.01) –

rs35694136 Li D. [8] 2006 Pancreas USA Mixed 307/329*b – 0.87 (0.63–1.18)

Olivieri EH [80] 2009 Head andNeck

Brasil Mixed 81/134*b – 8.98(4.49–17.93)

Pavanello S. [50] 2010 Bladder Italy Caucasian 167/141*b – 0.73 (0.46–1.14)

Singh A. [31] 2010 Lung India Caucasian 200/200*a – 1.65(1.11–2.45)

Pavanello S. [30] 2012 Lung Denmark Caucasian 415/760*a – 0.98 (0.65–1.49)

Ayari I. [34] 2013 Breast Tunisia Caucasian 108/38*b – 0.88 (0.40–1.93)

Statistically significant results are presented in bold. °OR (95 % CI) Odds Ratio and 95 % Confidence Interval 1Ten European countries: Denmark, France, Germany,Greece, Italy, the Netherlands, Norway, Spain, Sweden, and the United Kingdom. 2Six European countries: Romania, Hungary, Poland, Russia, Slovakia, CzechRepublic. 3Ten European countries: Czech Republic, Germany, Greece, Italy, Ireland, Norway, United Kingdom, Spain, Croatia, France. *Hardy-Weinberg Equilibrium(HWE), P value ˃0.01. aPopulation-based study bHospital-based study cGenome-wide Association Study. (a), (b) One study with two different population


the CYP1A2 polymorphism and cancer risk according toethnicity, source of controls and sample size and thenstratified by cancer type. We found a significant OR of0.79 (95 % CI 0.65–0.95; P for heterogeneity = 0.09, I2 =58.1 %) for bladder cancer among the hospital-basedpopulation and among Caucasians. There was no signifi-cant association among Caucasians for breast cancer(OR = 1.71; 95 % CI 0.94–3.10; P for heterogeneity < 0.01,I2 = 83.4 %), lung cancer (OR = 1.07; 95 % CI 0.79–1.44; Pfor heterogeneity = 0.07, I2 = 48.1 %,) or colorectal cancer(OR = 1.05, 95 % CI 0.94–1.16; P for heterogeneity = 0.49,I2 = 0.0 %). Among Asians, when stratifying for cancertype, we obtained an OR of 0.76 (95 % CI 0.47–1.22; P forheterogeneity = 0.48, I2 = 0.0 %) for colorectal cancer andOR = 1.27 (95 % CI 0.75–2.16; P for heterogeneity <0.01,I2 = 83.6 %) for breast cancer.

When pooling the 20 studies on rs2069514, the meta-analysis provided an OR of 0.99 (95 % CI 0.81–1.21) foroverall cancer (P for heterogeneity <0.01; I2 = 60 %)(Fig. 2). Egger test and the Begg’s correlation methodprovided no statistical evidence of publication bias (P =0.86 and P = 0.56, respectively). We performed the Gal-braith’s test to explore the source of heterogeneity andaccordingly singled out the study of B’chir F. et al. [24]as the main contributor to heterogeneity (graph notshown). In the one-way sensitivity analysis, the study ofB’chir F. et al. [24] was omitted from the overallmeta-analysis and the heterogeneity dropped down to14 % (P = 0.28), with the OR of 0.93 (95 % CI 0.82–1.06).We evaluated the effect of the rs2069514 polymorphism

according to the tumour site and obtained an OR of 0.96(95 % CI 0.65–1.43; P for heterogeneity = 0.07, I2 = 53.2 %)

Fig. 2 Forest plot of the CYP1A2 rs762551 and cancer meta-analysis under recessive models of inheritance. The diamonds and horizontal linescorrespond to the study-specific odds ratio (OR) and 95 % confidence interval (CI)


for colorectal cancer, an OR of 1.29 (95 % CI 0.60–2.79; Pfor heterogeneity = 0.00; I2 = 82.1 %) for lung cancer(Table 2). Analyses on different ethnicity and study designdid not provide any significant results (Caucasians OR =1.16; 95 % CI 0.63–2.14; I2 = 75.7 %, P < 0.01, for AsiansOR = 0.96; 95 % CI 0.86–1.07, I2 = 0.0 %; P = 0.86 andHospital-based study design OR = 1.01; 95 % CI 0.73–1.40; I2 = 73.7 %, P < 0.01, for Population-based design OR= 0.94; 95 % CI 0.78–1.14; I2 = 10.6 %, P = 0.35). We didnot observe any significant association between rs2069514polymorphism and cancer risk when subgrouping data ac-cording to ethnicity, source of controls and sample sizeand then stratified by cancer type. Among Caucasians,we obtained an OR of 1.28 (95 % CI 0.55–2.98; I2 =

80.9 %, P < 0.01) for lung cancer, while among AsiansOR = 0.94 (95 % CI 0.68–1.31; I2 = 0.0 %, P = 0.44) forlung and OR = 0.94 (95 % CI 0.71–1.24; I2 = 28.8 %, P= 0.25) for colorectal cancer.We performed meta-analysis of 11 studies on

rs2470890 which provided an OR of 1.11 (95 % CI 0.96-1.28) for the overall cancer risk (P for heterogeneity0.09; I2 = 39 %) (Fig. 2). Egger test and the Begg’s correl-ation method provided no statistical evidence of publica-tion bias (P = 0.42 and P = 0.59, respectively). TheGalbraith’s test singled out the study of Anderson LN etal. [33] as the main contributor to heterogeneity (graphnot shown). In one-way sensitivity analysis, this studywas omitted from the overall meta-analysis and the

Fig. 3 Forest plot of the remaining five CYP1A2 SNPs and cancer meta-analyses under different models of inheritance. The diamonds and horizontallines correspond to the study-specific odds ratio (OR) and 95 % confidence interval (CI)


heterogeneity dropped down to 6 % (P = 0.39), with stillnot significant OR of 1.06 (95 % CI, 0.94–1.19). The ef-fect of rs2470890 polymorphism according to thetumour site was also evaluated and was obtained non-significant result of OR of 1.10 (95 % CI, 0.94–1.28) Pfor heterogeneity = 0.51, I2 = 0.0 % for colorectal cancerand an OR of 1.20 (95 % CI, 0.83–1.74), P for heterogen-eity = 0.09; I2 = 65.7 % for cancer of upper aero-digestivetract (UADT) (Table 2). Subgroups analyses by differentethnicity showed a significant association betweenrs2470890 polymorphism and cancer for Mixed popula-tion OR = 1.44; 95 % CI 1.16–1.80; I2 = 0.0 %, P = 0.41,while not among Caucasians (OR = 1.07; 95 % CI 0.96–1.20; I2 = 0.0 %, P = 0.41) nor Asians (OR = 0.77; 95 % CI0.37–1.64; I2 = 55.4 %, P = 0.13).Results of the remaining three SNPs of CYP1A2 are

presented in the Fig. 3 and the Table 2. Absence of sig-nificant association with overall risk of cancer was re-ported. Only for rs2472304 we rendered an OR of 0.72(95 % CI 0.52–0.99) I2 = 0.0 %, P = 0.61 for Caucasians,when doing a subgroup analyses on ethnicity. No evi-dence of significant heterogeneity was detected (data notshown).When the meta-analyses were performed excluding

small sample size studies for all examined SNPs, therewere still no significant results obtained for the associ-ation between CYP1A2 SNPs and cancer risk (Table 2).

DiscussionThe current meta-analysis included 71 studies with morethan 47,000 cancer cases and 58,000 controls, detailingon all the CYP1A2 gene polymorphisms and risk of can-cer, shows no significant effect of investigated CYP1A2

SNPs on cancer overall risk under various geneticmodels. Meta-analysis is a common tool for summariz-ing different studies to resolve the problem of small sizestatistical power and discrepancy in genetic associationstudies [85] and also it provides more reliable resultsthan a single case-control study. To the best of ourknowledge, this is the largest and most comprehensivemeta-analysis on CYP1A2 SNPs and cancer performedso far. Several previous meta-analyses have been re-ported on the association between CYP1A2 gene poly-morphisms and risk of cancer [86–95]. Deng et al. [87]reported no association between CYP1A2 rs762551 poly-morphism and lung cancer risk by including 1675 casesand 2393 controls. In the paper of Xue et al. [94], com-bined mutational homozygous and wild type homozy-gous genotype compared with mutational heterozygousgenotype, had protective effect against gastric cancer byincluding 383 cases and 1229 controls. Wen-Xia Sun etal. [91] reported a significant protective effect of homo-zygous mutant of rs762551 CYP1A2 SNP on bladdercancer in Caucasian population. Based on 19 studies,Wang et al. [93] found a borderline significantly in-creased risk of overall cancer among homozygous mu-tant of CYP1A2 rs762551, mainly in Caucasians. Themeta-analysis of 46 case-control studies by Tian et al.[92] suggested that the wild-type allele of CYP1A2rs762551 polymorphism might be associated with breastand ovarian cancer risk, especially among Caucasians.These inconclusive results could be explained by differ-ences in study design, sample size, ethnicity, and cancersubtypes included.The CYP1A2 gene is a member of the CYP1 family

and is involved in metabolism of carcinogens and

Fig. 4 Funnel plot for publication bias for studies with CYP1A2 rs762551. Each point represents an individual study for the indicated association


Table 2 Subgroup meta-analyses of CYP1A2 SNPs and cancer risk according to study design, ethnicity and tumour site

Number cases/controls Recessive model

Exposed Not exposed OR° 95 % CI° I2 (%) P value forheterogeneity

rs762551 3373/4006 29,808/36,562 1.03 0.96–1.12 50.4 <0.01

Study design

Hospital based 1048/1110 8289/8482 1.03 0.88–1.20 60.3 <0.01

Population based 2314/2869 21,326/27,820 1.05 0.96–1.15 41.8 <0.01

Study sample size

Large 2883/3387 27,381/32,680 1.02 0.94–1.11 55.9 <0.01

Small 490/619 2427/3882 1.09 0.90–1.32 36.0 0.05

Ethnicity

Asian 348/414 2539/2874 0.95 0.72–1.27 54.6 0.02

Caucasian 2132/2600 18,305/23,388 1.03 0.94–1.13 42.4 <0.01

Mixed 893/992 8964/10,300 1.05 0.89–1.25 62.8 <0.01

Tumour site

Bladder 392/436 3038/2806 0.84 0.70–1.01 28.5 0.23

Breast 1097/1280 10,285/13,269 1.17 0.94–1.45 79.2 <0.01

Colorectum 803/934 7755/9199 1.03 0.93–1.14 0.0 0.56

Endometrium 258/391 1095/1898 1.06 0.87–1.30 0.0 0.85

Liver 12/26 235/330 0.63 0.30–1.32 5.0 0.31

Lung 221/265 1536/2446 1.20 0.87–1.64 58.9 0.01

Ovaries 27/34 349/478 1.31 0.33–5.19 80.3 0.01

Pancreas 107/142 1296/1656 1.04 0.80–1.36 0.0 0.87

Stomach 46/141 425/1258 0.85 0.59–1.21 0.0 0.45

UADT 186/192 1670/1631 0.97 0.73–1.29 29.9 0.23

Number cases/controls Dominant model

Exposed Not exposed OR 95 % CI I2 (%) P value forheterogeneity

rs2069514 1229/1373 3333/5026 0.99 0.81–1.21 60.0 <0.01

Study design

Hospital based 758/783 1727/2329 1.01 0.73–1.40 73.7 <0.01

Population based 471/590 1606/2697 0.94 0.78–1.14 10.6 0.35

Study sample size

Large 969/1085 2691/3736 0.97 0.86–1.09 0.0 0.89

Small 260/288 642/1290 1.18 0.65–2.11 81.1 <0.01

Ethnicity

Asian 1093/1235 1297/1388 0.96 0.86–1.07 0.0 0.86

Caucasian 136/138 2036/3638 1.16 0.63–2.14 75.7 <0.01

Tumour site

Bladder 236/237 352/326 0.92 0.73–1.17 0.0 0.81

Colorectum 447/458 836/847 0.96 0.65–1.43 53.2 0.07

Liver 315/409 324/393 0.94 0.76–1.15 0.0 0.68

Lung 211/219 1397/1881 1.16 0.68–1.99 76.3 <0.01




Table 2 Subgroup meta-analyses of CYP1A2 SNPs and cancer risk according to study design, ethnicity and tumour site (Continued)

rs2069526 139/202 1486/2144 0.94 0.70–1.26 20.7 0.27

Study design

Hospital based 35/78 706/1236 0.89 0.47–1.72 53.9 0.09


Study sample size

Large 121/151 1137/1181 0.85 0.56–1.28 49.4 0.12

Small 18/51 349/963 1.29 0.71–2.35 0.0 0.89

Ethnicity

Caucasian 139/202 1486/2144 0.94 0.70–1.26 20.7 0.27

Tumour site

Colorectum 74/93 737/788 0.91 0.66–1.26 0.0 0.40

Lung 60/75 676/811 0.90 0.47–1.71 55.4 0.08



rs2470890 1106/1187 4559/5538 1.11 0.96–1.28 39.0 0.09

Study design

Hospital based 429/480 2594/3069 1.10 0.95–1.27 0.0 0.46


Study sample size

Large 1077/1043 4390/4714 1.11 0.94–1.30 50.9 0.04

Small 29/144 169/824 1.07 0.69–1.66 0.0 0.85

Ethnicity

Asian 28/42 873/975 0.77 0.37–1.64 55.4 0.13

Caucasian 863/957 2904/3525 1.07 0.96–1.20 0.0 0.47

Mixed 215/188 782/1038 1.44 1.16–1.80 0.0 0.41

Tumour site

Breast 222/189 866/1025 1.17 0.65–2.08 73.4 0.05

Colorectum 500/509 866/929 1.10 0.94–1.28 0.0 0.51

Lung 48/77 348/520 0.92 0.63–1.37 0.0 0.47

UADT 294/262 1535/1528 1.20 0.83–1.74 65.7 0.09



rs2472304 127/172 975/1107 0.84 0.64–1.09 0.0 0.43

Study design


Study sample size

Large 112/136 878/846 0.79 0.59–1.05 0.0 0.41

Ethnicity

Caucasian 92/121 346/383 0.72 0.52–0.99 0.0 0.61

Tumour site

Breast 42/52 714/712 0.94 0.61–1.45 0.0 0.43




estrogens. In particular, it plays an essential role in themetabolic activation of pro-carcinogens, such as polycyc-lic aromatic hydrocarbons (PAHs) and heterocyclic aro-matic amines (HAA) [93]. Therefore, increased levels ofthis enzyme could explain the association with increasedrisk for cancer [16]. The wild genotype of CYP1A2*1 Frepresents a highly inducible genotype, and this highCYP1A2 activity may increase the hydroxylated formsas proximate carcinogens, from HCAs and aryl-amines [29].In our meta-analyses, we showed that none of the in-

vestigated CYP1A2 polymorphisms were significantly as-sociated with overall risk of cancer at various sites.These results confirm the findings of a recent meta-analysis from Li Zhenzhen et al. [95] where was reportedno significant associations with cancer risk in any gen-etic model (allele contrast, codominant, dominant, or re-cessive model) in terms of rs2069514 and rs3569413.For rs762551, they found that carriers of C-allele havean increased overall risk of developing cancer in allelegenetic model (C-allele vs. A-allele) while not in othermodels. Their further subgroup analyses demonstratedthat rs762551 polymorphism was associated with an in-creased risk of cancer in Caucasians under dominantmodel, while we investigated rs762551 under recessivemodel and did not obtain significant association. More-over, their meta-analysis included only 37 case-controlstudies of rs762551 involving 16,825 cancer cases and21,513 controls. Our meta-analysis may be the mostcomprehensive meta-analysis of the relationship betweenthe CYP1A2 rs762551 polymorphisms and the risk ofcancer, to date.When stratifying according to tumour site, our results

showed a borderline not significant OR of 0.84 (95 % CI,0.70–1.01) for bladder cancer for those homozygous mu-tant of rs762551 with total of 3430 cases and 3242 con-trols included (Table 2), thus confronting the previous

evidence from Wen-Xia Sun et al. [91] that reported anOR = 0.79 (95 % CI 0.66–0.94) from 2415 cases and2208 controls, and suggesting that on even bigger num-ber of subjects investigated, this significance might dis-appear. Pavanello et al. [96] stressed that polymorphismsof rs762551 might be the crucial modulating factor alongthe continuum from the exposure to relevant environ-mental and occupational factors, in increased CYP1A2activity of smokers measured by the urinary caffeinemetabolic ratio.We also found a significant decreased risk for bladder

cancer for mutant carriers of rs762551 among thehospital-based population. Hospital-based studies havecertain biases since those controls may have some be-nign diseases which can progress and also may not berepresentative of the general population. Using apopulation-based control would reduce the chance ofbias in these studies.In one recent meta-analysis by Zhi-Bin Bu et al. [86]

on the association between CYP1A2 rs762551,rs2069514, rs2069526, and rs2470890 polymorphismsand lung cancer risk, there was no evidence of signifi-cant association between lung cancer risk and CYP1A2rs2069514, s2470890, and rs2069526 polymorphisms.They found increased lung cancer risk for rs762551polymorphism in Caucasians from 3 studies, while inour analysis there was no such connection on a biggersample of studies [24, 26–28, 30–32].Lastly, when stratifying our results for breast and colo-

rectal cancer, we did not report any significant associ-ation between rs762551 and these cancers, thusconfirming previous meta-analyses of Li-Xin Qiu et al.[90] on breast and Xiao-Feng He et al. [88] on colorectalcancer risk. Other meta-analysis by Jianbing Hu et al.[89] also suggested that CYP1A2 rs762551 polymorph-ism was not a risk factor for colorectal cancer suscepti-bility, since no association was detected after all studies

Table 2 Subgroup meta-analyses of CYP1A2 SNPs and cancer risk according to study design, ethnicity and tumour site (Continued)

rs35694136 439/419 839/1183 1.37 0.78–2.42 89.0 <0.01

Study design

Hospital based 290/263 373/379 1.46 0.56–3.77 92.7 <0.01


Study sample size

Large 290/319 632/970 1.11 0.75–1.64 69.8 0.04

Small 149/100 207/213 1.78 0.37–8.60 94.6 <0.01

Ethnicity

Caucasian 255/241 635/898 1.04 0.70–1.53 62.0 0.05

Mixed 184/178 204/285 2.73 0.28–27.09 97.3 <0.01

Tumour site

Lung 149/156 466/804 1.28 0.77–2.13 68.4 0.08

Statistically significant ORs are presented in bold. °OR (95 % CI) Odds Ratio and 95 % Confidence Interval


were pooled together nor in a subgroup analysis by eth-nicity or source of controls, in all genetic models. Theinfluence of the different CYP1A2 SNPs might becamouflaged by the presence of some yet unidentifiedcausal genes involved in many other types of cancer.When stratifying the results according to ethnicity, the

protective effect of rs2472304 in our study was restrictedonly to Caucasians, while for rs2470890, we noticed an in-creased risk among a mixed population. A possible ex-planation for these results could be that the samepolymorphisms may play different roles in cancer suscep-tibility in different ethnic populations as well as differenttumour positions, due to a difference in genetic back-grounds, the environment they live in, lifestyle and migra-tions, which all may have a critical role in cancerpathogenesis [97]. Also, some low penetrance genetic ef-fects of single polymorphism could be determined by theirinteraction with other polymorphisms and/or a specificenvironmental exposure.No other relevant results were reported for the

remaining SNPs, however there were available only fewstudies regarding these associations, involving relativelysmall number of participants.In interpreting the results, some limitations of our study

should be considered. Firstly, only published studies wereincluded, so there was space for publication bias, which infact was confirmed by formal statistical tests. Secondly,the study size for most of the CYP1A2 polymorphismswas limited to perform any meaningful subgroup analyses.Thirdly, it would have been valuable to stratify the resultsaccording to environmental effect modifiers, though thiswas not possible, as the original data sets were not avail-able. Indeed, due to lack of access to original data used inincluded studies, our meta-analyses are based on the un-adjusted data, so the effects might be confounded ormodified by relevant covariates. Fourthly, besidebreast cancer, there are no genome-wide associationstudies of the effects of CYP1A2 polymorphisms oncancer risk. We were able to include only one breastcancer GWAS into our analyses, therefore our resultsmight be affected by additional publication bias.Despite these limitations, our meta-analyses also have

some advantages. First, the statistical power of the ana-lyses was noticeably increased as a huge number of casesand controls were pooled from different studies and hasmore statistical powerful than any single case-controlstudy. Secondly, in our analyses, we included more stud-ies than any previously published meta-analysis on theassociation between CYP1A2 polymorphism and cancerrisks and investigated 6 different CYP1A2 SNPs.

ConclusionsIn conclusion, our meta-analysis suggests that investi-gated CYP1A2 polymorphisms are not associated with

cancer susceptibility under various genetic models. Inorder to reach a more definitive conclusion, there is anecessity for further gene-gene and gene-environmentinteraction studies to be conducted on different popula-tions and larger sample size, for diverse CYP1A2 SNPs.

Abbreviations95 % CI: 95 % confidence interval; CYP1A2: cytochrome P450 1A2;dbGaP: The database of Genotypes and Phenotypes; GAME-ON: GeneticAssociations and Mechanisms in Oncology; GWAS: genome-wide associationstudies; GWAS DB: The genome wide association database; HAA: heterocyclicaromatic amines; HAs: heterocyclic amines; HuGE: the Human GenomeEpidemiology Navigator; HWE: Hardy-Weinberg Equilibrium;mtmt: homozygous mutant genotype; NCBI: National Center forBiotechnology Information; NHGRI: National Human Genome ResearchInstitute Catalog; ORs: Odds Ratios; PAHs: polycyclic aromatic hydrocarbons;PIs: principal investigators; SNPs: single nucleotide polymorphisms;VaDE: VarySysDB Disease Edition; wtmt: wild-type mutant-type heterozygousgenotype; wtwt: wild-type homozygous genotype.

Competing interestsThe authors declare that they have no competing interests.

Authors’ contributionsVV, MRG, SB made concept and design of the study. VV, RA, CI, MRG, SBdeveloped the methodology and contributed to data extraction. Statisticalanalysis and interpretation of data was done by VV, EL, RP and SB. Draftingthe manuscript was done by VV, CI, RP, SB. All authors read and approvedthe final manuscript

AcknowledgementsThe Authors would like to thank Professor John Hopper and his workingteam from the Centre for Epidemiology and Biostatistics, Melbourne Schoolof Population and Global Health, The University of Melbourne, for providingus with the primary GWAS data from their study of association betweenCYP1A2 SNPs and breast cancer risk. Also, would like to thank theERAWEB mobility programme, under the European Commission, forfinancially supporting the work of VV., the Fondazione Veronesi forsupporting the work of Emanuele Leoncini, and the Associazione Italiana perla Ricerca sul Cancro (AIRC) for supporting the work of Roberta Pastorino.

Received: 8 April 2015 Accepted: 28 January 2016

References1. Boffetta P, Boccia S, La Vecchia C. A Quick Guide to Cancer Epidemiology.

Springer International Publishing; 2014. p. 11–4.2. Heller F. Genetics/genomics and drug effects. Acta Clin Belg. 2013;68(2):77–80.3. Raunio H, Husgafvel-Pursiainen K, Anttila S, Hietanen E, Hirvonen A,

Pelkonen O. Diagnosis of polymorphisms in carcinogen-activating andinactivating enzymes and cancer susceptibility–a review. Gene. 1995;159(1):113–21.

4. Altayli E, Gunes S, Yilmaz AF, Goktas S, Bek Y. CYP1A2, CYP2D6, GSTM1,GSTP1, and GSTT1 gene polymorphisms in patients with bladder cancer in aTurkish population. Int Urol Nephrol. 2009;41(2):259–66.

5. Sachse C, Bhambra U, Smith G, Lightfoot TJ, Barrett JH, Scollay J, et al.Polymorphisms in the cytochrome P450 CYP1A2 gene (CYP1A2) incolorectal cancer patients and controls: allele frequencies, linkagedisequilibrium and influence on caffeine metabolism. Br J Clin Pharmacol.2003;55(1):68–76.

6. Nakajima M, Yokoi T, Mizutani M, Kinoshita M, Funayama M, Kamataki T.Genetic polymorphism in the 5′-flanking region of human CYP1A2 gene:effect on the CYP1A2 inducibility in humans. J Biochem. 1999;125(4):803–8.

7. Aldrich MC, Selvin S, Hansen HM, Barcellos LF, Wrensch MR, Sison JD, et al.CYP1A1/2 haplotypes and lung cancer and assessment of confounding bypopulation stratification. Cancer Res. 2009;69(6):2340–8.

8. Li D, Jiao L, Li Y, Doll MA, Hein DW, Bondy ML, et al. Polymorphisms ofcytochrome P4501A2 and N-acetyltransferase genes, smoking, and risk ofpancreatic cancer. Carcinogenesis. 2006;27(1):103–11.


9. Bae SY, Choi SK, Kim KR, Park CS, Lee SK, Roh HK, et al. Effects of geneticpolymorphisms of MDR1, FMO3 and CYP1A2 on susceptibility to colorectalcancer in Koreans. Cancer Sci. 2006;97(8):774–9.

10. Cleary SP, Cotterchio M, Shi E, Gallinger S, Harper P. Cigarette smoking,genetic variants in carcinogen-metabolizing enzymes, and colorectal cancerrisk. Am J Epidemiol. 2010;172(9):1000–14.

11. Cotterchio M, Boucher BA, Manno M, Gallinger S, Okey AB, Harper PA. Redmeat intake, doneness, polymorphisms in genes that encode carcinogen-metabolizing enzymes, and colorectal cancer risk. Cancer EpidemiolBiomarkers Prev. 2008;17(11):3098–107.

12. Dik VK, van Oijen MG, Uiterwaal C, van Gils C, van Duijnhoven FJ, Cauchi S,et al. Coffee consumption, genetic polymorphisms in CYP1A2 and NAT2,and colorectal cancer risk. Gastroenterology. 2013;144(5):S589–90.

13. Kiss I, Orsos Z, Gombos K, Bogner B, Csejtei A, Tibold A, et al. Associationbetween allelic polymorphisms of metabolizing enzymes (CYP 1A1, CYP1A2, CYP 2E1, mEH) and occurrence of colorectal cancer in Hungary.Anticancer Res. 2007;27(4C):2931–7.

14. Kobayashi M, Otani T, Iwasaki M, Natsukawa S, Shaura K, Koizumi Y, et al.Association between dietary heterocyclic amine levels, geneticpolymorphisms of NAT2, CYP1A1, and CYP1A2 and risk of colorectal cancer:a hospital-based case-control study in Japan. Scand J Gastroenterol.2009;44(8):952–9.

15. Kury S, Buecher B, Robiou-du-Pont S, Scoul C, Sebille V, Colman H, et al.Combinations of cytochrome P450 gene polymorphisms enhancing the riskfor sporadic colorectal cancer related to red meat consumption. CancerEpidemiol Biomarkers Prev. 2007;16(7):1460–7.

16. Landi S, Gemignani F, Moreno V, Gioia-Patricola L, Chabrier A, Guino E, et al.A comprehensive analysis of phase I and phase II metabolism genepolymorphisms and risk of colorectal cancer. Pharmacogenet Genomics.2005;15(8):535–46.

17. Rudolph A, Sainz J, Hein R, Hoffmeister M, Frank B, Forsti A, et al.Modification of menopausal hormone therapy-associated colorectal cancerrisk by polymorphisms in sex steroid signaling, metabolism and transportrelated genes. Endocr Relat Cancer. 2011;18(3):371–84.

18. Sachse C, Smith G, Wilkie MJ, Barrett JH, Waxman R, Sullivan F, et al. Apharmacogenetic study to investigate the role of dietary carcinogens in theetiology of colorectal cancer. Carcinogenesis. 2002;23(11):1839–49.

19. Saebo M, Skjelbred CF, Brekke Li K, Bowitz Lothe IM, Hagen PC, Johnsen E,et al. CYP1A2 164 A–>C polymorphism, cigarette smoking, consumption ofwell-done red meat and risk of developing colorectal adenomas andcarcinomas. Anticancer Res. 2008;28(4C):2289–95.

20. Sainz J, Rudolph A, Hein R, Hoffmeister M, Buch S, von Schonfels W, et al.Association of genetic polymorphisms in ESR2, HSD17B1, ABCB1, and SHBGgenes with colorectal cancer risk. Endocr Relat Cancer. 2011;18(2):265–76.

21. Wang J, Joshi AD, Corral R, Siegmund KD, Marchand LL, Martinez ME, et al.Carcinogen metabolism genes, red meat and poultry intake, and colorectalcancer risk. Int J Cancer. 2012;130(8):1898–907.

22. Yeh CC, Sung FC, Tang R, Chang-Chieh CR, Hsieh LL. Polymorphisms ofcytochrome P450 1A2 and N-acetyltransferase genes, meat consumption,and risk of colorectal cancer. Dis Colon Rectum. 2009;52(1):104–11.

23. Yoshida K, Osawa K, Kasahara M, Miyaishi A, Nakanishi K, Hayamizu S, et al.Association of CYP1A1, CYP1A2, GSTM1 and NAT2 gene polymorphismswith colorectal cancer and smoking. Asian Pac J Cancer Prev.2007;8(3):438–44.

24. B’Chir F, Pavanello S, Knani J, Boughattas S, Arnaud MJ, Saguem S. CYP1A2genetic polymorphisms and adenocarcinoma lung cancer risk in theTunisian population. Life Sci. 2009;84(21–22):779–84.

25. Chiou HL, Wu MF, Chien WP, Cheng YW, Wong RH, Chen CY, et al. NAT2fast acetylator genotype is associated with an increased risk of lung canceramong never-smoking women in Taiwan. Cancer Lett. 2005;223(1):93–101.

26. Gemignani F, Landi S, Szeszenia-Dabrowska N, Zaridze D, Lissowska J,Rudnai P, et al. Development of lung cancer before the age of 50: the roleof xenobiotic metabolizing genes. Carcinogenesis. 2007;28(6):1287–93.

27. Gervasini G, Ghotbi R, Aklillu E, San Jose C, Cabanillas A, Kishikawa J, et al.Haplotypes in the 5′-untranslated region of the CYP1A2 gene are inverselyassociated with lung cancer risk but do not correlate with caffeinemetabolism. Environ Mol Mutagen. 2013;54(2):124–32.

28. Mikhalenko AP, Krupnova EV, Chakova NN, Chebotareva NV, Demidchik YE.Assessment of the relationship between combinations of polymorphicvariants of xenobiotic-metabolizing enzyme genes and predisposition tolung cancer. Cytol Genet. 2014;48(2):111–6.

29. Osawa Y, Osawa KK, Miyaishi A, Higuchi M, Tsutou A, Matsumura S, et al.NAT2 and CYP1A2 polymorphisms and lung cancer risk in relation tosmoking status. Asian Pac J Cancer Prev. 2007;8(1):103–8.

30. Pavanello S, Fedeli U, Mastrangelo G, Rota F, Overvad K, Raaschou-NielsenO, et al. Role of CYP1A2 polymorphisms on lung cancer risk in a prospectivestudy. Cancer Genet. 2012;205(6):278–84.

31. Singh AP, Pant MC, Ruwali M, Shah PP, Prasad R, Mathur N, et al.Polymorphism in cytochrome P450 1A2 and their interaction with riskfactors in determining risk of squamous cell lung carcinoma in men. CancerBiomark. 2010;8(6):351–9.

32. Zienolddiny S, Campa D, Lind H, Ryberg D, Skaug V, Stangeland LB, et al. Acomprehensive analysis of phase I and phase II metabolism genepolymorphisms and risk of non-small cell lung cancer in smokers.Carcinogenesis. 2008;29(6):1164–9.

33. Anderson LN, Cotterchio M, Mirea L, Ozcelik H, Kreiger N. Passive cigarettesmoke exposure during various periods of life, genetic variants, and breastcancer risk among never smokers. Am J Epidemiol. 2012;175(4):289–301.

34. Ayari I, Fedeli U, Saguem S, Hidar S, Khlifi S, Pavanello S. Role of CYP1A2polymorphisms in breast cancer risk in women. Mol Med Rep. 2013;7(1):280–6.

35. Gulyaeva LF, Mikhailova ON, PustyInyak VO, Kim IV, Gerasimov AV,Krasilnikov SE, et al. Comparative analysis of SNP in estrogen-metabolizingenzymes for ovarian, endometrial, and breast cancers in Novosibirsk, Russia.Adv Exp Med Biol. 2008;617:359–66.

36. Hopper JL, Dite GS, Jenkins MA, Southey MC, Hocking JS, Giles GG, et al.Familial risks, early-onset breast cancer, and BRCA1 and BRCA2 germlinemutations. J Natl Cancer Inst. 2003;95(6):448–57.

37. Khvostova EP, Pustylnyak VO, Gulyaeva LF. Genetic polymorphism ofestrogen metabolizing enzymes in Siberian women with breast cancer.Genet Test Mol Biomarkers. 2012;16(3):167–73.

38. Kotsopoulos J, Ghadirian P, El-Sohemy A, Lynch HT, Snyder C, Daly M, et al.The CYP1A2 genotype modifies the association between coffeeconsumption and breast cancer risk among BRCA1 mutation carriers.Cancer Epidemiol Biomarkers Prev. 2007;16(5):912–6.

39. Le Marchand L, Donlon T, Kolonel LN, Henderson BE, Wilkens LR. Estrogenmetabolism-related genes and breast cancer risk: the multiethnic cohortstudy. Cancer Epidemiol Biomarkers Prev. 2005;14(8):1998–2003.

40. Lee HJ, Wu K, Cox DG, Hunter D, Hankinson SE, Willett WC, et al.Polymorphisms in xenobiotic metabolizing genes, intakes of heterocyclicamines and red meat, and postmenopausal breast cancer. Nutr Cancer.2013;65(8):1122–31.

41. Long JR, Egan KM, Dunning L, Shu XO, Cai Q, Cai H, et al. Population-basedcase-control study of AhR (aryl hydrocarbon receptor) and CYP1A2polymorphisms and breast cancer risk. Pharmacogenet Genomics.2006;16(4):237–43.

42. Lowcock EC, Cotterchio M, Anderson LN, Boucher BA, El-Sohemy A. Highcoffee intake, but not caffeine, is associated with reduced estrogen receptornegative and postmenopausal breast cancer risk with no effect modificationby CYP1A2 genotype. Nutr Cancer. 2013;65(3):398–409.

43. Marie-Genica C. Genetic polymorphisms in phase I and phase II enzymesand breast cancer risk associated with menopausal hormone therapy inpostmenopausal women. Breast Cancer Res Treat. 2010;119(2):463–74.

44. Sangrajrang S, Sato Y, Sakamoto H, Ohnami S, Laird NM, Khuhaprema T, etal. Genetic polymorphisms of estrogen metabolizing enzyme and breastcancer risk in Thai women. Int J Cancer. 2009;125(4):837–43.

45. Shimada N, Iwasaki M, Kasuga Y, Yokoyama S, Onuma H, Nishimura H, et al.Genetic polymorphisms in estrogen metabolism and breast cancer risk incase-control studies in Japanese, Japanese Brazilians and non-JapaneseBrazilians. J Hum Genet. 2009;54(4):209–15.

46. Takata Y, Maskarinec G, Le Marchand L. Breast density and polymorphismsin genes coding for CYP1A2 and COMT: the Multiethnic Cohort. BMCCancer. 2007;7:30.

47. Cui X, Lu X, Hiura M, Omori H, Miyazaki W, Katoh T. Association ofgenotypes of carcinogen-metabolizing enzymes and smoking status withbladder cancer in a Japanese population. Environ Health Prev Med.2013;18(2):136–42.

48. Figueroa JD, Malats N, Garcia-Closas M, Real FX, Silverman D, Kogevinas M,et al. Bladder cancer risk and genetic variation in AKR1C3 and othermetabolizing genes. Carcinogenesis. 2008;29(10):1955–62.

49. Guey LT, Garcia-Closas M, Murta-Nascimento C, Lloreta J, Palencia L,Kogevinas M, et al. Genetic susceptibility to distinct bladder cancersubphenotypes. Eur Urol. 2010;57(2):283–92.


50. Pavanello S, Mastrangelo G, Placidi D, Campagna M, Pulliero A, Carta A, etal. CYP1A2 polymorphisms, occupational and environmental exposures andrisk of bladder cancer. Eur J Epidemiol. 2010;25(7):491–500.

51. Tsukino H, Kuroda Y, Nakao H, Imai H, Inatomi H, Osada Y, et al.Cytochrome P450 (CYP) 1A2, sulfotransferase (SULT) 1A1, and N-acetyltransferase (NAT) 2 polymorphisms and susceptibility to urothelialcancer. J Cancer Res Clin Oncol. 2004;130(2):99–106.

52. Villanueva CM, Silverman DT, Murta-Nascimento C, Malats N, Garcia-ClosasM, Castro F, et al. Coffee consumption, genetic susceptibility and bladdercancer risk. Cancer Causes Control. 2009;20(1):121–7.

53. National Center for Biotechnology Information. dbSNP Home Page. [cited2015 02/15]. Available from: http://www.ncbi.nlm.nih.gov/SNP/index.html.Accessed 15 Feb 2015.

54. Attia J, Thakkinstian A, D’Este C. Meta-analyses of molecular associationstudies: methodologic lessons for genetic epidemiology. J Clin Epidemiol.2003;56(4):297–303.

55. DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials.1986;7(3):177–88.

56. Deeks J, Altman D, Bradburn M. Statistical methods for examiningheterogeneity and combining results from several studies in meta-analysis. In:Egger M, Davey Smith G, Altman D, editors. Systematic reviews in health care:meta-analysis in context. 2nd ed. London: BMJ Books; 2001. p. 285–312.

57. Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency inmeta-analyses. BMJ. 2003;327(7414):557–60.

58. Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysisdetected by a simple, graphical test. BMJ. 1997;315(7109):629–34.

59. Ioannidis JP, Trikalinos TA. The appropriateness of asymmetry tests forpublication bias in meta-analyses: a large survey. CMAJ. 2007;176(8):1091–6.

60. Galbraith RF. A note on graphical presentation of estimated odds ratiosfrom several clinical trials. Stat Med. 1988;7(8):889–94.

61. Agudo A, Sala N, Pera G, Capella G, Berenguer A, Garcia N, et al.Polymorphisms in metabolic genes related to tobacco smoke and the riskof gastric cancer in the European prospective investigation into cancer andnutrition. Cancer Epidemiol Biomarkers Prev. 2006;15(12):2427–34.

62. Ashton KA, Proietto A, Otton G, Symonds I, McEvoy M, Attia J, et al.Polymorphisms in genes of the steroid hormone biosynthesis andmetabolism pathways and endometrial cancer risk. Cancer Epidemiol.2010;34(3):328–37.

63. Barbieri RB, Bufalo NE, Cunha LL, Assumpcao LV, Maciel RM, Cerutti JM, et al.Genes of detoxification are important modulators of hereditary medullarythyroid carcinoma risk. Clin Endocrinol. 2013;79(2):288–93.

64. Canova C, Hashibe M, Simonato L, Nelis M, Metspalu A, Lagiou P, et al.Genetic associations of 115 polymorphisms with cancers of the upperaerodigestive tract across 10 European countries: the ARCAGE project.Cancer Res. 2009;69(7):2956–65.

65. Canova C, Richiardi L, Merletti F, Pentenero M, Gervasio C, Tanturri G, et al.Alcohol, tobacco and genetic susceptibility in relation to cancers of theupper aerodigestive tract in northern Italy. Tumori. 2010;96(1):1–10.

66. Chen X, Wang H, Xie W, Liang R, Wei Z, Zhi L, et al. Association of CYP1A2genetic polymorphisms with hepatocellular carcinoma susceptibility: a case-control study in a high-risk region of China. Pharmacogenet Genomics.2006;16(3):219–27.

67. De Roos AJ, Gold LS, Wang S, Hartge P, Cerhan JR, Cozen W, et al.Metabolic gene variants and risk of non-Hodgkin’s lymphoma. CancerEpidemiol Biomarkers Prev. 2006;15(9):1647–53.

68. Doherty JA, Weiss NS, Freeman RJ, Dightman DA, Thornton PJ, Houck JR,et al. Genetic factors in catechol estrogen metabolism in relation to the risk ofendometrial cancer. Cancer Epidemiol Biomarkers Prev. 2005;14(2):357–66.

69. Eom SY, Yim DH, Zhang Y, Yun JK, Moon SI, Yun HY, et al. Dietary aflatoxin B1intake, genetic polymorphisms of CYP1A2, CYP2E1, EPHX1, GSTM1, and GSTT1,and gastric cancer risk in Korean. Cancer Causes Control. 2013;24(11):1963–72.

70. Ferlin A, Ganz F, Pengo M, Selice R, Frigo AC, Foresta C. Association oftesticular germ cell tumor with polymorphisms in estrogen receptor andsteroid metabolism genes. Endocr Relat Cancer. 2010;17(1):17–25.

71. Gemignani F, Neri M, Bottari F, Barale R, Canessa PA, Canzian F, et al. Risk ofmalignant pleural mesothelioma and polymorphisms in genes involved in thegenome stability and xenobiotics metabolism. Mutat Res. 2009;671(1–2):76–83.

72. Ghoshal U, Tripathi S, Kumar S, Mittal B, Chourasia D, Kumari N, et al.Genetic polymorphism of cytochrome P450 (CYP) 1A1, CYP1A2, and CYP2E1genes modulate susceptibility to gastric cancer in patients withHelicobacter pylori infection. Gastric Cancer. 2014;17(2):226–34.

73. Goodman MT, McDuffie K, Kolonel LN, Terada K, Donlon TA, Wilkens LR,et al. Case-control study of ovarian cancer and polymorphisms in genesinvolved in catecholestrogen formation and metabolism. Cancer EpidemiolBiomarkers Prev. 2001;10(3):209–16.

74. Goodman MT, Tung KH, McDuffie K, Wilkens LR, Donlon TA. Association ofcaffeine intake and CYP1A2 genotype with ovarian cancer. Nutr Cancer.2003;46(1):23–9.

75. Hirata H, Hinoda Y, Okayama N, Suehiro Y, Kawamoto K, Kikuno N, et al.CYP1A1, SULT1A1, and SULT1E1 polymorphisms are risk factors forendometrial cancer susceptibility. Cancer. 2008;112(9):1964–73.

76. Imaizumi T, Higaki Y, Hara M, Sakamoto T, Horita M, Mizuta T, et al.Interaction between cytochrome P450 1A2 genetic polymorphism andcigarette smoking on the risk of hepatocellular carcinoma in a Japanesepopulation. Carcinogenesis. 2009;30(10):1729–34.

77. Jang JH, Cotterchio M, Borgida A, Gallinger S, Cleary SP. Genetic variants incarcinogen-metabolizing enzymes, cigarette smoking and pancreatic cancerrisk. Carcinogenesis. 2012;33(4):818–27.

78. Kobayashi M, Otani T, Iwasaki M, Natsukawa S, Shaura K, Koizumi Y, et al.Association between dietary heterocyclic amine levels, genetic polymorphismsof NAT2, CYP1A1, and CYP1A2 and risk of stomach cancer: a hospital-basedcase-control study in Japan. Gastric Cancer. 2009;12(4):198–205.

79. Mochizuki J, Murakami S, Sanjo A, Takagi I, Akizuki S, Ohnishi A. Geneticpolymorphisms of cytochrome P450 in patients with hepatitis C virus-associatedhepatocellular carcinoma. J Gastroenterol Hepatol. 2005;20(8):1191–7.

80. Olivieri EH, da Silva SD, Mendonca FF, Urata YN, Vidal DO, Faria Mde A, et al.CYP1A2*1C, CYP2E1*5B, and GSTM1 polymorphisms are predictors of riskand poor outcome in head and neck squamous cell carcinoma patients.Oral Oncol. 2009;45(9):e73–9.

81. Prawan A, Kukongviriyapan V, Tassaneeyakul W, Pairojkul C, BhudhisawasdiV. Association between genetic polymorphisms of CYP1A2, arylamine N-acetyltransferase 1 and 2 and susceptibility to cholangiocarcinoma. Eur JCancer Prev. 2005;14(3):245–50.

82. Rebbeck TR, Troxel AB, Wang Y, Walker AH, Panossian S, Gallagher S, et al.Estrogen sulfation genes, hormone replacement therapy, and endometrialcancer risk. J Natl Cancer Inst. 2006;98(18):1311–20.

83. Shahabi A, Corral R, Catsburg C, Joshi AD, Kim A, Lewinger JP, et al. Tobaccosmoking, polymorphisms in carcinogen metabolism enzyme genes, and risk oflocalized and advanced prostate cancer: results from the CaliforniaCollaborative Prostate Cancer Study. Cancer Med. 2014;3(6):1644–55.

84. Suzuki H, Morris JS, Li Y, Doll MA, Hein DW, Liu J, et al. Interaction of thecytochrome P4501A2, SULT1A1 and NAT gene polymorphisms withsmoking and dietary mutagen intake in modification of the risk ofpancreatic cancer. Carcinogenesis. 2008;29(6):1184–91.

85. Munafo MR, Flint J. Meta-analysis of genetic association studies. TrendsGenet. 2004;20(9):439–44.

86. Bu ZB, Ye M, Cheng Y, Wu WZ. Four polymorphisms in the cytochromeP450 1A2 (CYP1A2) gene and lung cancer risk: a meta-analysis. Asian Pac JCancer Prev. 2014;15(14):5673–9.

87. Deng SQ, Zeng XT, Wang Y, Ke Q, Xu QL. Meta-analysis of the CYP1A2 -163C>Apolymorphism and lung cancer risk. Asian Pac J Cancer Prev. 2013;14(5):3155–8.

88. He XF, Wei J, Liu ZZ, Xie JJ, Wang W, Du YP, et al. Association betweenCYP1A2 and CYP1B1 polymorphisms and colorectal cancer risk: a meta-analysis. PLoS One. 2014;9(8), e100487.

89. Hu J, Liu C, Yin Q, Ying M, Li J, Li L, et al. Association between the CYP1A2-164 A/C polymorphism and colorectal cancer susceptibility: a meta-analysis.Mol Genet Genomics. 2014;289(3):271–7.

90. Qiu LX, Yao L, Mao C, Yu KD, Zhan P, Chen B, et al. Lack of association ofCYP1A2-164 A/C polymorphism with breast cancer susceptibility: a meta-analysis involving 17,600 subjects. Breast Cancer Res Treat. 2010;122(2):521–5.

91. Sun WX, Chen YH, Liu ZZ, Xie JJ, Wang W, Du YP, et al. Association betweenthe CYP1A2 polymorphisms and risk of cancer: a meta-analysis. Mol GenetGenomics. 2015;290(2):709–25.

92. Tian Z, Li YL, Zhao L, Zhang CL. Role of CYP1A2 1F polymorphism in cancerrisk: evidence from a meta-analysis of 46 case-control studies. Gene.2013;524(2):168–74.

93. Wang H, Zhang Z, Han S, Lu Y, Feng F, Yuan J. CYP1A2 rs762551polymorphism contributes to cancer susceptibility: a meta-analysis from 19case-control studies. BMC Cancer. 2012;12:528.

94. Xue H, Lu Y, Xue Z, Lin B, Chen J, Tang F, et al. The effect of CYP1A1 andCYP1A2 polymorphisms on gastric cancer risk among different ethnicities: asystematic review and meta-analysis. Tumour Biol. 2014;35(5):4741–56.


http://www.ncbi.nlm.nih.gov/SNP/index.html

95. Zhenzhen L, Xianghua L, Ning S, Zhan G, Chuanchuan R, Jie L. Currentevidence on the relationship between three polymorphisms in the CYP1A2gene and the risk of cancer. Eur J Cancer Prev. 2013;22(6):607–19.

96. Pavanello S, Pulliero A, Lupi S, Gregorio P, Clonfero E. Influence of the geneticpolymorphism in the 5′-noncoding region of the CYP1A2 gene on CYP1A2phenotype and urinary mutagenicity in smokers. Mutat Res. 2005;587(1–2):59–66.

97. Hirschhorn JN, Lohmueller K, Byrne E, Hirschhorn K. A comprehensivereview of genetic association studies. Genet Med. 2002;4(2):45–61.

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